Method and System for Multi-Part Left Atrium Segmentation in C-Arm Computed Tomography Volumes Using Shape Constraints

ABSTRACT

A method and system for multi-part left atrium (LA) segmentation in a C-arm CT volume is disclosed. Multiple LA part models, including an LA chamber body mesh, an appendage mesh, a left inferior pulmonary vein (PV) mesh, a left superior PV mesh, a right inferior PV mesh, and a right superior PV mesh, are segmented in a  3 D volume. The LA chamber body mesh and the appendage mesh may be segmented as a combined object and the PV meshes may be segmented subject to a statistical shape constraint. A consolidated LA mesh is generated from the segmented LA part models.

This application claims the benefit of U.S. Provisional Application No.61/451,028, filed Mar. 9, 2011, the disclosure of which is hereinincorporated by reference.

BACKGROUND OF THE INVENTION

The present invention relates to cardiac imaging, and more particularly,to left atrium segmentation in C-arm computed tomography (CT) images.

Strokes are the third leading cause of death in the United States.Approximately fifteen percent of all strokes are caused by atrialfibrillation (AF). As a widely used minimally invasive surgery to treatAF, a catheter based ablation procedure uses high radio-frequency energyto eliminate sources of ectopic foci, especially around the ostia of theappendage and the pulmonary veins (PV). Automatic segmentation of theleft atrium (LA) is important for pre-operative assessment to identifythe potential sources of electric events. However, there are largevariations in PV drainage patterns between different patients. Forexample, the most common variations, which are found in 20-30% of thepopulation, are extra right PVs and left common PVs (where two left PVsmerge into one before joining the chamber).

Conventional LA segmentation methods can be roughly categorized asnon-model based or model-based approaches. The non-model basedapproaches do not assume any prior knowledge of the LA shape and thewhole segmentation procedure is purely data driven. An advantage ofnon-model based methods is that they can handle structural variations ofthe PVs. However, such methods cannot provide the underlying anatomicalinformation (e.g., which part of the segmentation is the left inferiorPV). In practice non-model based approaches work well on computedtomography (CT) or magnetic resonance imaging (MRI) data, but suchmethods are typically not robust on challenging C-arm CT images. Modelbased approaches exploit a prior shape of the LA (either in the form ofan atlas or a mean shape mesh) to guide the segmentation. Using a priorshape constraint typically allows model based approaches to avoidleakage around weak or missing boundaries, which plagues non-model basedapproaches. However, using one mean shape, it is difficult to handlestructural variations (e.g., the left common PV). In order to address PVvariations, multiple atlases are required, which costs extra computationtime.

BRIEF SUMMARY OF THE INVENTION

The present invention provides a method and system for automaticallysegmenting the left atrium (LA) in C-arm CT image data. Embodiments ofthe present invention utilize a part based LA model including thechamber, appendage, and four major pulmonary veins (PVs). Embodiments ofthe present invention use a model based approach to segment the LA partsand enforce a statistical shape constraint during estimation of poseparameters of the different parts.

In one embodiment of the present invention, an LA chamber body mesh, anappendage mesh, and a plurality of PV meshes are segmented in a 3Dvolume. The PV meshes may include a left inferior PV mesh, a leftsuperior PV mesh, a right inferior PV mesh, and a right superior PVmesh. The LA chamber body mesh and the appendage mesh may be segmentedas a combined object and the PV meshes may be segmented subject to astatistical shape constraint. A consolidated LA mesh is generated fromthe segmented LA chamber body mesh, appendage mesh, and PV meshes.

These and other advantages of the invention will be apparent to those ofordinary skill in the art by reference to the following detaileddescription and the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a part-based left atrium model according to anembodiment of the present invention;

FIG. 2 illustrates a method for part-based segmentation of the leftatrium according to an embodiment of the present invention;

FIG. 3 illustrates a method of segmenting the left atrium parts in a 3Dvolume according to an embodiment of the present invention;

FIG. 4 illustrates a method for segmenting the pulmonary veins using astatistical shape constrained according to an embodiment of the presentinvention;

FIG. 5 illustrates exemplary left atrium chamber, appendage, andpulmonary vein segmentation results;

FIG. 6 illustrates a method for generating a consolidated mesh from theleft atrium part meshes according to an embodiment of the presentinvention;

FIG. 7 illustrates exemplary results of the method steps of FIG. 6;

FIG. 8 illustrates exemplary left atrium segmentation results; and

FIG. 9 is a high level block diagram of a computer capable ofimplementing the present invention.

DETAILED DESCRIPTION

The present invention is directed to a method and system for fullyautomatic segmentation of the left atrium (LA) in C-arm CT image data.Embodiments of the present invention are described herein to give avisual understanding of the LA segmentation method. A digital image isoften composed of digital representations of one or more objects (orshapes). The digital representation of an object is often describedherein in terms of identifying and manipulating the objects. Suchmanipulations are virtual manipulations accomplished in the memory orother circuitry/hardware of a computer system. Accordingly, it is to beunderstood that embodiments of the present invention may be performedwithin a computer system using data stored within the computer system.

Embodiments of the present invention provide fully automatic LAsegmentation in C-arm CT data. Compared to conventional CT or MRI, anadvantage of C-arm CT is that overlay of the 3D patient-specific LAmodel onto a 2D fluoroscopic image is straightforward and accurate sinceboth the 3D and 2D images are captured on the same device within a shorttime interval. Typically, a non-electrocardiography-gated acquisition isperformed to reconstruct a C-arm CT volume. Accordingly, the C-arm CTvolume often contains severe motion artifacts. For a C-arm imageacquisition device with a small X-ray detector panel, part of apatient's body may be missing in some 2D X-ray projections due to thelimited field of view, resulting in significant artifacts around themargin of a reconstructed volume. In addition, there may be severestreak artifacts caused by various catheters inserted in the heart.These challenges are addressed herein using a model based approach forLA segmentation, which also takes advantage a machine learning basedobject pose detector and boundary detector.

Instead of using one mean model, the challenge of pulmonary vein (PV)structural variations is addressed in embodiments of the presentinvention using a part based model, where the whole LA is split into thechamber, appendage, and four major PVs. Each part is a much simpleranatomical structure compared to the holistic LA structure. Therefore,each part can be detected and segmented using a model based approach. Inorder to increase robustness, embodiments of the present inventiondetect the most reliable structure (the LA chamber) and use it toconstrain the detection of other parts (the appendage and PVs). Inparticular, the robustness of detecting the appendage can be increasedby segmenting the LA chamber model and the appendage model as a singleobject. Due to large variations, the relative position of the PVs to theLA chamber varies significantly. In an advantageous embodiment, astatistical shape model is used to enforce a shape constraint during theestimation of PV pose parameters (position, orientation, and size).

FIG. 1 illustrates a part-based LA model according to an embodiment ofthe present invention. As shown in image (a) of FIG. 1, the part-basedLA model 100 includes the LA chamber body 102, appendage 104, and fourmajor PVs 106, 108, 110, and 112. The four major PVs are the leftinferior PV 112, the left superior PV 110, the right inferior PV 108,and the right superior PV 106. The shape of the appendage 104 is closeto a tilted cone and the PVs 106, 108, 110, and 112 each have a tubularstructure. Since, for atrial fibrillation (AF) ablation, physicianstypically only care about a proximal PV trunk, the each PV model 106,108, 110, and 112 only detects a trunk of 20 mm in length originatingfrom its respective ostium. Each LA part 102, 104, 106, 108, 110, and112 is a much simpler anatomical structure as compared to a holistic LAstructure, and therefore can be detected and segmented using a modelbased approach. Once the LA parts are segmented in a C-arm CT volume,they are combined into a consolidated mesh model. Image (b) of FIG. 1shows a consolidated LA mesh 120 including the LA chamber 122, appendage124, and PVs 126, 128, 130, and 132. Image (c) of FIG. 1 shows theoverlay of a consolidated LA mesh 140 including the LA chamber 142,appendage 144, and PVs 146, 148, 150, and 152 on a 2D fluoroscopicimage.

FIG. 2 illustrates a method for part-based segmentation of the LAaccording to an embodiment of the present invention. As illustrated inFIG. 2, at step 202, a 3D medical image volume is received. In anadvantageous embodiment, the 3D medical image volume is a C-arm CTvolume, but the present invention is not limited thereto and may besimilarly applied to other types of 3D volumes, such as conventional CTand MRI volumes, as well. The 3D medical image volume may be receiveddirectly from an image acquisition device, such as a C-arm imageacquisition device. It is also possible that the 3D medical image volumeis received by loading a 3D medical image volume stored on a storage ormemory of a computer system.

At step 204, the LA parts are segmented in the 3D medical image volume.In particular, the LA chamber body, appendage, left inferior PV, leftsuperior PV, right inferior PV, and right superior PV are segmented inthe 3D medical image volume, resulting in a patient-specific mesh foreach of the parts. Marginal Space Learning (MSL) can be used to segmenteach of the LA chamber mesh, the appendage mesh, and the PV meshes inthe 3D volume.

MSL is used to estimate the position, orientation, and scale of anobject in a 3D volume using a series of detectors trained usingannotated training data. In order to efficiently localize an objectusing MSL, parameter estimation is performed in a series of marginalspaces with increasing dimensionality. Accordingly, the idea of MSL isnot to learn a classifier directly in the full similarity transformationspace, but to incrementally learn classifiers in the series of marginalspaces. As the dimensionality increases, the valid space region becomesmore restricted by previous marginal space classifiers. A 3D objectdetection (object pose estimation) is split into three steps: objectposition estimation, position-orientation estimation, andposition-orientation-scale estimation. A separate classifier is trainedbased on annotated training data for each of these steps. This objectlocalization stage results in an estimated transformation (position,orientation, and scale) of the object, and a mean shape of the object isaligned with the 3D volume using the estimated transformation. After theobject pose estimation, the boundary of the object is refined using alearning based boundary detector. MSL is described in greater detail inU.S. Pat. No. 7,916,919, issued Mar. 29, 2011, and entitled “System andMethod for Segmenting Chambers of a Heart in a Three Dimensional Image”,which is incorporated herein by reference.

For each LA part (chamber body, appendage, and each PV), an MSL basedpose detector (including position, position-orientation, andposition-orientation-scale detectors) and a learning based boundarydetector are trained based on annotated training data. The traineddetectors for each LA part can be used to segment a separate mesh foreach LA part in the 3D volume. Compared to a holistic approach for LAsegmentation, the part based approach can handle large structuralvariations. The MSL based segmentation works well for the LA chamber.However, independent detection of the other parts may not be robust,either due to low contrast (appendage) or small object size (PVs).Accordingly, an advantageous embodiment of the present invention,described in FIG. 3 below, uses constrained detection of the LA parts.In particular, the detection of the appendage and the PVs may beconstrained by the LA chamber body.

FIG. 3 illustrates a method of segmenting the LA parts in a 3D volumeaccording to an embodiment of the present invention. FIG. 3 canadvantageously be used to implement step 204 of FIG. 2. At step 302, theLA chamber body and the appendage are segmented as a combined object. InC-arm CT, the appendage is particularly difficult to detect. Theappendage is a pouch without an outlet and the blood flow inside theappendage is slow, which may prevent the appendage frame filling withcontrast agent. In many datasets, the appendage is only barely visible.The trained MSL detector for the appendage may detect the neighboringleft superior PV, which often touches the appendage and has a highercontrast. However, the relative position of the appendage to the chamberis quite consistent. Accordingly, a more robust detection is achieved bysegmenting the appendage mesh and the chamber mesh as a single object.In this case, one MSL based posed detector is trained to detect thecombined object.

At step 304, the PVs are segmented using a statistical shape constraint.Through comparison experiments, the present inventors have determinedthat neither a holistic approach, nor independent detection was robustin detecting the four PVs. An advantageous embodiment of the presentinvention enforces a shape constraint in detection of the PVs. A pointdistribution model (PDM) is often used to enforce a statistical shapeconstraint among a set of landmarks. The total variation of the shape isdecomposed into orthogonal deformation modes through principal componentanalysis (PCA). A deformed shape is projected into a low dimensionaldeformation subspace to enforce a statistical shape constraint.

FIG. 4 illustrates a method for segmenting the PVs using a statisticalshape constraint according to an embodiment of the present invention.The method of FIG. 4 can be used to implement step 304 of FIG. 3. Atstep 402, a pose for each PV is independently detected using MSL. Inparticular, for each PV (left inferior, left superior, right inferior,and right interior), a respective trained MSL detector estimates ninepose parameters: three position parameters (T_(x),T_(y),T_(z)), threeorientation Euler angles (O_(x),O_(y),O_(z)), and three anisotropicscaling parameters (S_(x),S_(y),S_(z)).

At step 404, a point distribution model is generated from the estimatedpose parameters of the PV. Different from the conventional PDM, whichenforces a shape constraint on a set of landmark points, in this casethe shape constraint must be enforced on the estimated orientation andsize of each PV. One possible solution is to stack all of the PV poseparameters into a large vector to perform PCA. However, the position andorientation parameters are measured in different units. If not weightedproperly, the extracted deformation modes may be dominated by onecategory of transformation. Furthermore, the Euler angles are periodic(with a period of 2π), which prevents application of PCA.

An advantageous embodiment of the present invention utilizes a newrepresentation of the pose parameters in order to avoid the abovedescribed problems. The object pose can be fully represented by theobject center T together with three scaled orthogonal axes. Alternativeto the Euler angles, the object orientation can be represented as arotation matrix (R_(x),R_(y),R_(z)) and each column of R defines anaxis. The object pose parameters can be fully represented by afour-point set T,V_(x),V_(y),V_(z)), where:

V _(x) =T+S _(x) R _(x) , V _(y) =T+S _(y) R _(y) , V _(z) =T+S _(z) R_(z).   (1)

Using the above representation, the pose of each PV is represented as aset of four points. The four points essentially represent a center pointand three corner points of a bounding box defined by the poseparameters. In order to generate the PDM, the pose parameters estimatedat step 402 for each of the four PVs are converted to the four-pointrepresentation. In addition to the four points for each of the PVs, thecenter points of the detected LA chamber and appendage are also added tothe PDM in order to stabilize the detection. This results in a PDMhaving 18 points.

At step 406, the point distribution model is deformed to enforce astatistical shape constraint. An active shape model (ASM) is used toadjust the points representing the PV poses in order to enforce thestatistical shape model. The statistical shape constraint is learnedfrom PDMs constructed from the annotated LA parts (LA chamber,appendage, and PVs) in training volumes. The total variation of theshape is decomposed into orthogonal deformation modes through PCA. Afterthe patient-specific PDM representing the poses of the PVs is generated,the patient-specific PDM is projected into a subspace with eightdimensions (which covers about 75% of the total variation) to enforcethe statistical shape constraint.

At step 408, an adjusted pose is recovered for each of the PVs based onthe deformed point distribution model. After enforcing the statisticalshape constraint, the deformed four-point representation for a PV can beexpressed as: ({circumflex over (T)},{circumflex over(V)}_(x),{circumflex over (V)}_(y),{circumflex over (V)}_(z)). Theadjusted PV center is given by point {circumflex over (T)}. The adjustedorientation {circumflex over (R)} and scale Ŝ can be recovered by simpleinversion of Equation (1). However, the estimate {circumflex over (R)}is generally not a true rotation matrix {circumflex over(R)}^(T){circumflex over (R)}=I. Accordingly, the adjusted rotation isdetermined by calculating the nearest rotation matrix R_(O) to minimizethe sum squares in elements in the difference matrix R_(O)−{circumflexover (R)}, which is equivalent to:

$\begin{matrix}{{R_{O} = {\min\limits_{R}{{Trace}\left( {\left( {R - \hat{R}} \right)^{T}\left( {R - \hat{R}} \right)} \right)}}},} & (2)\end{matrix}$

subject to R_(O) ^(T)R_(O)=I. Here, Trace(.) is the sum of the diagonalelements. The optimal solution to Equation (2) is given by:

R _(O) ={circumflex over (R)}({circumflex over (R)} ^(T) {circumflexover (R)})^(−1/2).   (3)

This results in an adjusted pose for each of the four PVs. The adjustedpose for each PV can then be used to align the mean shape of eachrespective PV, and then the learning based boundary detector can beapplied to each PV, as described above. Furthermore, in a possibleimplementation, the method of FIG. 4 can be applied iteratively toestimate the poses for the PV, where the adjusted poses for the PVsdetermined in step 408 in one iteration can be used to constrain asearch region for the MSL-based detection of the PVs at step 402 in thenext iteration. In this case, the method steps of FIG. 4 can be repeateduntil the PV poses converge or for a predetermined number of iterations.

FIG. 5 illustrates exemplary LA chamber, appendage, and PV segmentationresults. Images (a) and (b) of FIG. 5 show segmentation results for apatient with separate left inferior and superior PVs and images (c) and(d) show segmentation results for a patient with a left common PV. Asshown in images (a) and (b), an LA chamber mesh 502, appendage mesh 504,left inferior PV mesh 506, left superior PV mesh 508, right inferior PVmesh 510, and right superior PV mesh 512 are successfully segmented fora patient with separate left inferior and superior PVs. As shown inimages (c) and (d), an LA chamber mesh 522, appendage mesh 524, leftinferior PV mesh 526, left superior PV mesh 528, right inferior PV mesh530, and right superior PV mesh 532 are successfully segmented for apatient with a left common PV where the left inferior and superior PVsmerge into one before joining the chamber.

Returning to FIG. 2, at step 206, a consolidated mesh of the LA isgenerated from the segmented meshes of the LA parts. The constraineddetection and segmentation described above results in six meshes (the LAchamber mesh, appendage mesh, left inferior PV mesh, left superior PVmesh, right inferior PV mesh, and right superior PV mesh), as shown inimage (a) of FIG. 1. There may be gaps and/or intersections among thedifferent meshes. For use in AF ablation procedures, physicians likelyprefer a consolidated mesh with different anatomical structures labeledwith different structures.

FIG. 6 illustrates a method for generating a consolidated mesh from theLA part meshes according to an embodiment of the present invention. Themethod of FIG. 6 can be used to implement step 206 of FIG. 2. Asillustrated in FIG. 6, at step 602, each PV mesh and the appendage meshare projected to the LA chamber mesh. In particular, the proximal rim ofeach PV mesh and the appendage mesh is projected onto the LA chambermesh along the centerline of the respective mesh in order to eliminategaps between each PV mesh and the LA chamber mesh and gaps between theappendage mesh and the LA chamber mesh. This results in the meshes beingfully connected. FIG. 7 illustrates exemplary results of the methodsteps of FIG. 6. Image (a) of FIG. 7 illustrates separate meshessegmented for the LA chamber 702 and PVs 704, 706, 708, and 710. Image(b) of FIG. 7 shows PV meshes 714, 716, 718, and 720 having added meshpieces resulting from being projected to connect with the LA chambermesh 702.

Returning to FIG. 6, at step 604, the connected meshes are converted toa volume mask. After step 602, the meshes are fully connected. However,mesh intersections may still be present; pieces of one or more of the PVmeshes may lie inside the segmented LA chamber. Instead of workingdirectly on the meshes to resolve such intersections, the meshes areconverted to a volume mask. The volume mask is a binary mask in whichall voxels inside the 3D meshes are considered “positive” and all voxelsoutside of are considered “negative”. The volume mask can be generatedby assigning all positive voxels a predetermined intensity and allnegative voxels an intensity of zero. Referring to FIG. 7, image (c)shows a volume mask 730 generated from the meshes in image (b).

Returning to FIG. 6, at step 606, a consolidated mesh is generated fromthe volume mask. In particular, a new mesh is generated from the volumemask using the well-known marching cubes algorithm. The conversion ofthe connected meshes to a volume mask (step 604) and generation of a newmesh from the volume mask (step 606) removes an intersections in which aPV mesh protrudes in the LA chamber mesh and results in a consolidatedpatient-specific mesh showing the LA chamber, appendage, left inferiorPV, left superior PV, right inferior PV, and right superior PV.Referring to FIG. 7, image (d) shows a consolidated mesh 740. Theconsolidated mesh 740 provides a patient-specific segmentation of the LAchamber 742, appendage (not shown), left inferior PV 744, left superiorPV 746, right inferior PV 748, and right superior PV 750.

Returning to FIG. 2, at step 208, the LA segmentation results areoutput. For example, the consolidated mesh may be output by displayingthe consolidated mesh on a display device of a computer system or byoverlaying the consolidated mesh onto a 2D fluoroscopic image forguidance of a catheter ablation procedure (as shown in image (c) of FIG.1). The segmentation results may also be output be storing thesegmentation results on a storage or memory of a computer system.

FIG. 8 illustrates exemplary LA segmentation results using the abovedescribed methods. Images (a), (b), and (c) of FIG. 8 show exemplary LAsegmentation results in three different views of a large C-arm CTvolume. A shown in images (a), (b), and (c), a consolidated LA mesh 800is segmented in the volume, providing patient-specific segmentation ofthe LA chamber 802, appendage 804, left inferior PV 806, left superiorPV 808, right inferior PV 810, and right superior PV 812. Images (d),(e), and (f) of FIG. 8 show exemplary LA segmentation results in threedifferent views of a small C-arm CT volume. As shown in images (d), (e),and (f), a consolidated LA mesh 820 is segmented in the volume,providing patient-specific segmentation of the LA chamber 822, appendage824, left inferior PV 826, left superior PV 828, right inferior PV 830,and right superior PV 832.

The above-described methods for multi-part left atrium segmentation maybe implemented on a computer using well-known computer processors,memory units, storage devices, computer software, and other components.A high level block diagram of such a computer is illustrated in FIG. 9.Computer 902 contains a processor 904 which controls the overalloperation of the computer 902 by executing computer program instructionswhich define such operation. The computer program instructions may bestored in a storage device 912, or other computer readable medium (e.g.,magnetic disk, CD ROM, etc.) and loaded into memory 910 when executionof the computer program instructions is desired. Thus, the steps of themethods of FIGS. 2, 3, 4, and 6 may be defined by the computer programinstructions stored in the memory 910 and/or storage 912 and controlledby the processor 904 executing the computer program instructions. Animage acquisition device 920, such as a C-arm image acquisition device,can be connected to the computer 902 to input images to the computer902. It is possible to implement the image acquisition device 920 andthe computer 902 as one device. It is also possible that the imageacquisition device 920 and the computer 902 communicate wirelesslythrough a network. The computer 902 also includes one or more networkinterfaces 906 for communicating with other devices via a network. Thecomputer 902 also includes other input/output devices 908 that enableuser interaction with the computer 902 (e.g., display, keyboard, mouse,speakers, buttons, etc.). One skilled in the art will recognize that animplementation of an actual computer could contain other components aswell, and that FIG. 9 is a high level representation of some of thecomponents of such a computer for illustrative purposes.

The foregoing Detailed Description is to be understood as being in everyrespect illustrative and exemplary, but not restrictive, and the scopeof the invention disclosed herein is not to be determined from theDetailed Description, but rather from the claims as interpretedaccording to the full breadth permitted by the patent laws. It is to beunderstood that the embodiments shown and described herein are onlyillustrative of the principles of the present invention and that variousmodifications may be implemented by those skilled in the art withoutdeparting from the scope and spirit of the invention. Those skilled inthe art could implement various other feature combinations withoutdeparting from the scope and spirit of the invention.

1. A method of segmenting a left atrium (LA) in a 3D volume comprising:segmenting an LA chamber body mesh, an appendage mesh, and a pluralityof pulmonary vein (PV) meshes in the 3D volume; and generating aconsolidated LA mesh from the LA chamber body mesh, the appendage meshand the plurality of PV meshes.
 2. The method of claim 1, wherein theplurality of PV meshes includes a left inferior PV mesh, a left superiorPV mesh, a right inferior PV mesh, and a right superior PV mesh.
 3. Themethod of claim 1, wherein the step of segmenting an LA chamber bodymesh, an appendage mesh, and a plurality of pulmonary vein (PV) meshesin the 3D volume comprises: segmenting the LA chamber body mesh and theappendage mesh as a combined object; and segmenting the plurality of PVmeshes subject to a statistical shape constraint.
 4. The method of claim3, wherein the step of segmenting the LA chamber body mesh and theappendage mesh as a combined object comprises: segmenting the combinedobject including the LA chamber body mesh and the appendage mesh usingmarginal space learning (MSL).
 5. The method of claim 3, wherein thestep of segmenting the plurality of PV meshes subject to a statisticalshape constraint comprises: independently estimating pose parameters foreach of the plurality of PV meshes in the 3D volume using marginal spacelearning (MSL); generating a point distribution model based on the poseparameters and center points of the segmented LA chamber body andappendage meshes; and adjusting the pose parameters for the plurality ofPV meshes by enforcing a statistical shape constraint on the generatedpoint distribution model.
 6. The method of claim 5, wherein the step ofgenerating a point distribution model based on the pose parameters andcenter points of the segmented LA chamber body and appendage meshescomprises: for each of the plurality of PV meshes, converting the poseparameters estimated for the PV mesh to a set of four-point setrepresenting the estimated pose parameters, wherein the four-point setincludes a center point and three corner points of a bounding boxdefined by the estimated pose parameters for the PV mesh ; andgenerating the point distribution model including each of the four-pointsets representing the estimated pose parameters of the plurality of PVmeshes, the center point of the segmented chamber body mesh, and thecenter point of the segmented appendage mesh.
 7. The method of claim 6,wherein the step of adjusting the pose parameters for the plurality ofPV meshes by enforcing a statistical shape constraint on the generatedpoint distribution model comprises: deforming the point distributionmodel using an active shape model to enforce the statistical shapeconstraint; determining an adjusted pose for each of the plurality of PVmeshes based on the deformed point distribution model.
 8. The method ofclaim 1, wherein the step of generating a consolidated LA mesh from theLA chamber body mesh, the appendage mesh and the plurality of PV meshescomprises: connecting the LA chamber mesh with the appendage mesh andeach of the plurality of PV meshes by projecting the appendage mesh andeach of the plurality of PV meshes to the LA chamber mesh, resulting ina connected set of meshes; converting the connected set of meshes to avolume mask; and generating the consolidated LA mesh from the volumemask.
 9. The method of claim 8, wherein the step of generating theconsolidated LA mesh from the volume mask comprises: generating theconsolidated LA mesh from the volume mask using a marching cubesalgorithm.
 10. The method of claim 1, wherein the 3D volume is a 3DC-arm CT volume.
 11. An apparatus for segmenting a left atrium (LA) in a3D volume comprising: means for segmenting an LA chamber body mesh, anappendage mesh, and a plurality of pulmonary vein (PV) meshes in the 3Dvolume; and means for generating a consolidated LA mesh from the LAchamber body mesh, the appendage mesh and the plurality of PV meshes.12. The apparatus of claim 11, wherein the plurality of PV meshesincludes a left inferior PV mesh, a left superior PV mesh, a rightinferior PV mesh, and a right superior PV mesh.
 13. The apparatus ofclaim 12, wherein the means for segmenting an LA chamber body mesh, anappendage mesh, and a plurality of pulmonary vein (PV) meshes in the 3Dvolume comprises: means for segmenting the LA chamber body mesh and theappendage mesh as a combined object; and means for segmenting theplurality of PV meshes subject to a statistical shape constraint. 14.The apparatus of claim 13, wherein the means for segmenting the LAchamber body mesh and the appendage mesh as a combined object comprises:means for segmenting the combined object including the LA chamber bodymesh and the appendage mesh using marginal space learning (MSL).
 15. Theapparatus of claim 13, wherein the means for segmenting the plurality ofPV meshes subject to a statistical shape constraint comprises: means forindependently estimating pose parameters for each of the plurality of PVmeshes in the 3D volume using marginal space learning (MSL); means forgenerating a point distribution model based on the pose parameters andcenter points of the segmented LA chamber body and appendage meshes; andmeans for adjusting the pose parameters for the plurality of PV meshesby enforcing a statistical shape constraint on the generated pointdistribution model.
 16. The apparatus of claim 15, wherein the means forgenerating a point distribution model based on the pose parameters andcenter points of the segmented LA chamber body and appendage meshescomprises: means for converting the pose parameters estimated for a PVmesh to a set of four-point set representing the estimated poseparameters, wherein the four-point set includes a center point and threecorner points of a bounding box defined by the estimated pose parametersfor the PV mesh ; and means for generating the point distribution modelincluding the four-point sets representing the estimated pose parametersof each of the plurality of PV meshes, the center point of the segmentedchamber body mesh, and the center point of the segmented appendage mesh.17. The apparatus of claim 16, wherein the means for adjusting the poseparameters for the plurality of PV meshes by enforcing a statisticalshape constraint on the generated point distribution model comprises:means for deforming the point distribution model using an active shapemodel to enforce the statistical shape constraint; means for determiningan adjusted pose for each of the plurality of PV meshes based on thedeformed point distribution model.
 18. The apparatus of claim 11,wherein the means for generating a consolidated LA mesh from the LAchamber body mesh, the appendage mesh and the plurality of PV meshescomprises: means for connecting the LA chamber mesh with the appendagemesh and each of the plurality of PV meshes to result in a connected setof meshes; means for converting the connected set of meshes to a volumemask; and means for generating the consolidated LA mesh from the volumemask.
 19. A non-transitory computer readable medium encoded withcomputer executable instructions for segmenting a left atrium (LA) in a3D volume, the computer executable instructions defining a methodcomprising: segmenting an LA chamber body mesh, an appendage mesh, and aplurality of pulmonary vein (PV) meshes in the 3D volume; and generatinga consolidated LA mesh from the LA chamber body mesh, the appendage meshand the plurality of PV meshes.
 20. The non-transitory computer readablemedium of claim 19, wherein the plurality of PV meshes includes a leftinferior PV mesh, a left superior PV mesh, a right inferior PV mesh, anda right superior PV mesh.
 21. The non-transitory computer readablemedium of claim 19, wherein the step of segmenting an LA chamber bodymesh, an appendage mesh, and a plurality of pulmonary vein (PV) meshesin the 3D volume comprises: segmenting the LA chamber body mesh and theappendage mesh as a combined object; and segmenting the plurality of PVmeshes subject to a statistical shape constraint.
 22. The non-transitorycomputer readable medium of claim 21, wherein the step of segmenting theLA chamber body mesh and the appendage mesh as a combined objectcomprises: segmenting the combined object including the LA chamber bodymesh and the appendage mesh using marginal space learning (MSL).
 23. Thenon-transitory computer readable medium of claim 21, wherein the step ofsegmenting the plurality of PV meshes subject to a statistical shapeconstraint comprises: independently estimating pose parameters for eachof the plurality of PV meshes in the 3D volume using marginal spacelearning (MSL); generating a point distribution model based on the poseparameters and center points of the segmented LA chamber body andappendage meshes; and adjusting the pose parameters for the plurality ofPV meshes by enforcing a statistical shape constraint on the generatedpoint distribution model.
 24. The non-transitory computer readablemedium of claim 23, wherein the step of generating a point distributionmodel based on the pose parameters and center points of the segmented LAchamber body and appendage meshes comprises: for each of the pluralityof PV meshes, converting the pose parameters estimated for the PV meshto a set of four-point set representing the estimated pose parameters,wherein the four-point set includes a center point and three cornerpoints of a bounding box defined by the estimated pose parameters forthe PV mesh; and generating the point distribution model including eachof the four-point sets representing the estimated pose parameters of theplurality of PV meshes, the center point of the segmented chamber bodymesh, and the center point of the segmented appendage mesh.
 25. Thenon-transitory computer readable medium of claim 24, wherein the step ofadjusting the pose parameters for the plurality of PV meshes byenforcing a statistical shape constraint on the generated pointdistribution model comprises: deforming the point distribution modelusing an active shape model to enforce the statistical shape constraint;determining an adjusted pose for each of the plurality of PV meshesbased on the deformed point distribution model.
 26. The non-transitorycomputer readable medium of claim 19, wherein the step of generating aconsolidated LA mesh from the LA chamber body mesh, the appendage meshand the plurality of PV meshes comprises: connecting the LA chamber meshwith the appendage mesh and each of the plurality of PV meshes byprojecting the appendage mesh and each of the plurality of PV meshes tothe LA chamber mesh, resulting in a connected set of meshes; convertingthe connected set of meshes to a volume mask; and generating theconsolidated LA mesh from the volume mask.
 27. The non-transitorycomputer readable medium of claim 26, wherein the step of generating theconsolidated LA mesh from the volume mask comprises: generating theconsolidated LA mesh from the volume mask using a marching cubesalgorithm.